计算机工程与科学2017，Vol.39Issue(7)：1379-1384,6.DOI:10.3969/j.issn.1007-130X.2017.07.027
稀疏受限玻尔兹曼机研究综述
Review of sparse restricted Boltzmann machine
麦超 1邹维宝1
作者信息
- 1. 长安大学地质工程与测绘学院,陕西西安710054
- 折叠
摘要
Abstract
Sparse coding is used to describe the image features perceived in the human visual system.Sparse representation is a reasonable and effective representation of image features.We therefore introduce the restricted Boltzmann machine (RBM) to deep learning because of its reliable unsupervised ability to learn image features.Stacked sparse restricted Boltzmann machines (SRBMs) cannot only mimic the hierarchical organization of the cortex but also achieve more abstractive image features.So using the SRBM to obtain the sparse representation of image features attracts more attention in the field of AI.We introduce the basics of the RBM,describe its advantages and review thoroughly the existing work.Finallv,we summarize open questions suggested in the last section and the future development.关键词
稀疏表示/受限玻尔兹曼机/深度学习/图像处理Key words
sparse representation/RBM/deep learning/image processing分类
信息技术与安全科学引用本文复制引用
麦超,邹维宝..稀疏受限玻尔兹曼机研究综述[J].计算机工程与科学,2017,39(7):1379-1384,6.
